Time Series Analysis with Open Source Tools introduces the subject using R and Python programming and tools. This book assumes a basic understanding of statistics and mathematical or statistical modeling. Although a little programming experience would be nice, it is not required. There are a few “formulas,” with no theorems or proofs, and calculus never appears.Chapters one and two introduce the topic at hand with an overview and a brief discussion about the components of time series. R programming is introduced in Chapter 3 in the R-Studio environment with decomposing and analyzing the components of time series data using unemployment rate and consumer cost index over time as an example. It also introduces differencing and simple smoothing for making sense of the data and demonstrates the analysis of seasonality using beer sales. It introduces dealing with nonstationary time series data using loans as an example. Finally, it covers an alternative time series analysis method using R with airline passenger data.Chapter 4 introduces Python in the iPython environment for manipulating time series data. It covers working with data to format the time series, displaying and plotting the data, examining trend, and smoothing data using meat data from the U.S. Department of Agriculture. It also introduces loading and formatting data that is not native to Python add-ins.Later chapters cover the various application of time series analysis in several different industries including political, financial, and environmental. ARMA, ARIMA, and UCM methods and covered in detail, and GLARMA models are introduced.